Data Science Specialist

Your Impact: Lead the development and implementation of scalable big data solutions and advanced analytics frameworks for clients. Work closely with cross-functional teams to define business requirements and translate them into actionable data strategies. Design and optimize data models and algorithms that solve complex business challenges using large-scale datasets. Leverage big data technologies such as Hadoop, Spark, and NoSQL databases to process and analyze large datasets. Build and deploy machine learning models that can be integrated into production environments to deliver real-time insights. Conduct advanced statistical analysis and predictive modeling to uncover trends, patterns, and opportunities within big data. Continuously improve data science processes, ensuring they are efficient, scalable, and aligned with business objectives. Skills & Experience: 8+ years of experience in data science, big data engineering, or a similar field, with a strong understanding of analytics and machine learning. Proficiency with big data technologies such as Hadoop, Spark, Hive, and NoSQL databases (Cassandra, MongoDB, etc.). Strong programming skills in Python, Scala, R, or Java, with experience in libraries like Pandas, NumPy, Scikit-learn, TensorFlow, or similar. Expertise in statistical analysis, data mining, and predictive analytics techniques. Experience working with cloud platforms like AWS, Azure, or Google Cloud for big data processing and analytics. Ability to work with large datasets, performing ETL tasks and optimizing data pipelines for speed and efficiency. Strong problem-solving and critical thinking skills with the ability to make data-driven decisions in complex business environments. Set Yourself Apart With: Advanced degree (Master’s or PhD) in Data Science, Computer Science, Engineering, or a related field. Experience in deploying big data solutions at an enterprise level, particularly in industries like finance, healthcare, or retail. Familiarity with containerization technologies (Docker, Kubernetes) for deploying data science solutions. Experience with real-time data processing frameworks and stream processing (e.g., Apache Kafka, Apache Flink). Knowledge of business intelligence (BI) tools and the ability to present data insights to non-technical stakeholders.